Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations

Predictive value of hematocrit, serum albumin level difference, and fibrinogen-to-albumin ratio for COVID-19-associated acute respiratory failure

Background

Acute respiratory failure is the main clinical manifestation and a major cause of death in patients with COVID-19. However, few reports on its prevention and control have been published because of the need for laboratory predictive indicators. This study aimed to evaluate the predictive value of hematocrit level, serum albumin level difference, and fibrinogen-to-albumin ratio for COVID-19-associated acute respiratory failure.

Material and methods

A total of 120 patients with COVID-19 from the First Affiliated Hospital of Anhui Medical University were selected between December 2022 and March 2023. Patients were divided into acute respiratory failure and non-acute respiratory failure groups and compared patient-related indicators between them using univariate and multivariate logistic regression analyses. Receiver operating characteristic analysis was performed to determine the discrimination accuracy.

Results

In total, 48 and 72 patients were enrolled in the acute respiratory failure and non-acute respiratory failure groups, respectively. The Quick COVID-19 Severity Index scores, fibrinogen-to-albumin ratio, hematocrit and serum albumin level difference, fibrinogen, and hematocrit levels were significantly higher in the acute respiratory failure group than in the non-acute respiratory failure group. A Quick COVID-19 Severity Index >7, fibrinogen-to-albumin ratio >0.265, and hematocrit and serum albumin level difference >12.792 had a 96.14 % positive predictive rate and a 94.06 % negative predictive rate.

Conclusion

Both fibrinogen-to-albumin ratio and hematocrit and serum albumin level difference are risk factors for COVID-19-associated acute respiratory failure. The Quick COVID-19 Severity Index score combined with fibrinogen-to-albumin ratio, and hematocrit and serum albumin level difference predict high and low risks with better efficacy and sensitivity than those of the Quick COVID-19 Severity Index score alone; therefore, these parameters can be used collectively as a risk stratification method for assessing patients with COVID-19.

Safety, immunogenicity and protective effect of sequential vaccination with inactivated and recombinant protein COVID-19 vaccine in the elderly: a prospective longitudinal study

The safety and efficacy of COVID-19 vaccines in the elderly, a high-risk group for severe COVID-19 infection, have not been fully understood. To clarify these issues, this prospective study followed up 157 elderly and 73 young participants for 16 months and compared the safety, immunogenicity, and efficacy of two doses of the inactivated vaccine BBIBP-CorV followed by a booster dose of the recombinant protein vaccine ZF2001. The results showed that this vaccination protocol was safe and tolerable in the elderly. After administering two doses of the BBIBP-CorV, the positivity rates and titers of neutralizing and anti-RBD antibodies in the elderly were significantly lower than those in the young individuals. After the ZF2001 booster dose, the antibody-positive rates in the elderly were comparable to those in the young; however, the antibody titers remained lower. Gender, age, and underlying diseases were independently associated with vaccine immunogenicity in elderly individuals. The pseudovirus neutralization assay showed that, compared with those after receiving two doses of BBIBP-CorV priming, some participants obtained immunological protection against BA.5 and BF.7 after receiving the ZF2001 booster. Breakthrough infection symptoms last longer in the infected elderly and pre-infection antibody titers were negatively associated with the severity of post-infection symptoms. The antibody levels in the elderly increased significantly after breakthrough infection but were still lower than those in the young. Our data suggest that multiple booster vaccinations at short intervals to maintain high antibody levels may be an effective strategy for protecting the elderly against COVID-19.

Risk Factors for COVID-19 Infection in Adult Patients: A Retrospective Observational Study in Japan

Purpose

The aim of the study was to identify the characteristics of patients infected with coronavirus disease 2019 (COVID-19) and to determine risk factors for COVID-19 infection in Japanese patients.

Patients and Methods

We conducted a single-center retrospective observational study in Japanese adult patients (≥20 years) who visited Kenwakai Hospital (Nagano Japan). We analyzed data of 378 patients (mean age, 75 ± 14 years; men, 54%) from the hospital's electronic information system. COVID-19 was diagnosed by polymerase-chain reaction. Patients were divided into 2 groups based on diagnosis of COVID-19.

Results

Patients infected with COVID-19 showed significantly higher rates of men (69.8 vs 51.6%, P = 0.025) than uninfected control patients. After adjustment for possible confounding factors, COVID-19 infection was significantly associated with BUN (odds ratio [OR], 1.02; 95% confidence interval [CI], 1.01-1.03) and serum creatinine (Scr) (OR, 1.14; 95% CI, 1.05-1.24). This association was observed in men (BUN, P = 0.012; Scr, P = 0.012), but not in women (BUN, P = 0.43; Scr, P = 0.54).

Conclusion

BUN and Scr are potential risk factors for infection of COVID-19 in Japanese patients, particularly in men. Our results suggest that renal parameters might be important in Japanese male patients for the early detection of COVID-19 infection.

Variables Associated with 30-Day Mortality in Very Elderly COVID-19 Patients

BACKGROUND

Advanced age increases the risk for severe COVID-19. However, the risk factors for mortality from COVID-19 in very elderly patients (≥80-years-old) are unknown.

OBJECTIVE

Investigate the relationship of mortality with the clinical characteristics of very elderly COVID-19 patients.

MATERIALS AND METHODS

Very elderly patients who were hospitalized with COVID-19 from December 3, 2022 to January 1, 2023 were retrospectively examined. Sociodemographic and clinical variables were recorded and survival was recorded after 30 days.

RESULTS

We examined 181 patients (median age: 90.84 years; 114 older than 90 years). The median Barthel index was 30.69, and 55.8% of patients had severe or critical COVID-19 pneumonia. Forty-two patients (33.2%) received a high-flow nasal cannula or non-invasive ventilation, and only 4.4% received mechanical ventilation. The overall mortality was 35.9%, and there was no significant difference in mortality for the 80 to 90-year-old group and the over 90-year-old group (37.7% vs 32.8%, P=0.508). A multivariate analysis showed that the Barthel index (OR, 0.975; 95% CI, 0.962-0.989), serum creatinine (SCr) level (OR, 1.003; 95% CI, 1.000-1.006), white blood cell (WBC) count (OR, 1.160; 95% CI, 1.056-1.276), D-dimer level (OR, 1.060; 95% CI, 1.009-1.113), and corticosteroid use (OR, 0.268; 95% CI, 0.124-0.582) were significantly and independently related to 30-day mortality. A binary classification model based on the multivariate analysis had good predictive value (area under the curve, 0.794).

CONCLUSION

Very elderly COVID-19 patients have a high risk for mortality. The Barthel index, SCr, WBC count, D-dimer level, and corticosteroid use were independently associated with mortality.

Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK

Agent-based models have been proven to be quite useful in understanding and predicting the SARS-CoV-2 virus-originated COVID-19 infection. Person-to-person contact was considered as the main mechanism of viral transmission in these models. However, recent understanding has confirmed that airborne transmission is the main route to infection spread of COVID-19. We have developed a computationally efficient agent-based hybrid model to study the aerial propagation of the virus and subsequent spread of infection. We considered virus, a continuous variable, spreads diffusively in air and members of populations as discrete agents possessing one of the eight different states at a particular time. The transition from one state to another is probabilistic and age linked. Recognizing that population movement is a key aspect of infection spread, the model allows unbiased movement of agents. We benchmarked the model to recapture the temporal stochastic infection count data of the UK. The model investigates various key factors such as movement, infection susceptibility, new variants, recovery rate and duration, incubation period and vaccination on the infection propagation over time. Furthermore, the model was applied to capture the infection spread in Italy and France.

Risk and Protective Factors for COVID-19 Morbidity, Severity, and Mortality

The outbreak of the coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become an evolving global health crisis. Currently, a number of risk factors have been identified to have a potential impact on increasing the morbidity of COVID-19 in adults, including old age, male sex, pre-existing comorbidities, and racial/ethnic disparities. In addition to these factors, changes in laboratory indices and pro-inflammatory cytokines, as well as possible complications, could indicate the progression of COVID-19 into a severe and critical stage. Children predominantly suffer from mild illnesses due to COVID-19. Similar to adults, the main risk factors in pediatric patients include age and pre-existing comorbidities. In contrast, supplementation with a healthy diet and sufficient nutrition, COVID-19 vaccination, and atopic conditions may act as protective factors against the infection of SARS-CoV-2. COVID-19 vaccination not only protects vulnerable individuals from SARS-CoV-2 infection, more importantly, it may also reduce the development of severe disease and death due to COVID-19. Currently used therapies for COVID-19 are off-label and empiric, and their impacts on the severity and mortality of COVID-19 are still unclear. The interaction between asthma and COVID-19 may be bidirectional and needs to be clarified in more studies. In this review, we highlight the clinical evidence supporting the rationale for the risk and protective factors for the morbidity, severity, and mortality of COVID-19.

COVID-19 risk score as a public health tool to guide targeted testing: A demonstration study in Qatar

We developed a Coronavirus Disease 2019 (COVID-19) risk score to guide targeted RT-PCR testing in Qatar. The Qatar national COVID-19 testing database, encompassing a total of 2,688,232 RT-PCR tests conducted between February 5, 2020-January 27, 2021, was analyzed. Logistic regression analyses were implemented to derive the COVID-19 risk score, as a tool to identify those at highest risk of having the infection. Score cut-off was determined using the ROC curve based on maximum sum of sensitivity and specificity. The score’s performance diagnostics were assessed. Logistic regression analysis identified age, sex, and nationality as significant predictors of infection and were included in the risk score. The ROC curve was generated and the area under the curve was estimated at 0.63 (95% CI: 0.63–0.63). The score had a sensitivity of 59.4% (95% CI: 59.1%-59.7%), specificity of 61.1% (95% CI: 61.1%-61.2%), a positive predictive value of 10.9% (95% CI: 10.8%-10.9%), and a negative predictive value of 94.9% (94.9%-95.0%). The concept and utility of a COVID-19 risk score were demonstrated in Qatar. Such a public health tool can have considerable utility in optimizing testing and suppressing infection transmission, while maximizing efficiency and use of available resources.

Analyzing inherent biases in SARS-CoV-2 PCR and serological epidemiologic metrics

BACKGROUND

Prospective observational data show that infected persons with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain polymerase chain reaction (PCR) positive for a prolonged duration, and that detectable antibodies develop slowly with time. We aimed to analyze how these effects can bias key epidemiological metrics used to track and monitor SARS-CoV-2 epidemics.

METHODS

An age-structured mathematical model was constructed to simulate progression of SARS-CoV-2 epidemics in populations. PCR testing to diagnose infection and cross-sectional surveys to measure seroprevalence were also simulated. Analyses were conducted on simulated outcomes assuming a natural epidemic time course and an epidemic in presence of interventions.

RESULTS

The prolonged PCR positivity biased the epidemiological measures. There was a lag of 10 days between the true epidemic peak and the actually-observed peak. Prior to epidemic peak, PCR positivity rate was twofold higher than that based only on current active infection, and half of those tested positive by PCR were in the prolonged PCR positivity stage after infection clearance. Post epidemic peak, PCR positivity rate poorly predicted true trend in active infection. Meanwhile, the prolonged PCR positivity did not appreciably bias estimation of the basic reproduction number R0. The time delay in development of detectable antibodies biased measured seroprevalence. The actually-observed seroprevalence substantially underestimated true prevalence of ever infection, with the underestimation being most pronounced around epidemic peak.

CONCLUSIONS

Caution is warranted in interpreting PCR and serological testing data, and any drawn inferences need to factor the effects of the investigated biases for an accurate assessment of epidemic dynamics.

Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several challenges including data availability and quality, the applicability of existing models to those contexts, the accurate modelling of cultural differences or specificities of those operational settings, the communication of results and uncertainties, as well as the alignment of strategic goals between diverse partners in complex situations. In this paper, we systematically review the limited epidemiological modelling work applied to refugee and IDP settlements so far, and discuss challenges and identify lessons learnt from the process. With the likelihood of disease outbreaks expected to increase in the future as more people are displaced due to conflict and climate change, we call for the development of more approaches and models specifically designed to include the unique features and populations of refugee and IDP settlements. To strengthen collaboration between the modelling and the humanitarian public health communities, we propose a roadmap to encourage the development of systems and frameworks to share needs, build tools and coordinate responses in an efficient and scalable manner, both for this pandemic and for future outbreaks.

Characterizing the effective reproduction number during the COVID-19 pandemic: Insights from Qatar’s experience

Background

The effective reproduction number, R_t, is a tool to track and understand pandemic dynamics. This investigation of R_t estimations was conducted to guide the national COVID-19 response in Qatar, from the onset of the pandemic until August 18, 2021.

Methods

Real-time “empirical” R_t^Empirical was estimated using five methods, including the Robert Koch Institute, Cislaghi, Systrom-Bettencourt and Ribeiro, Wallinga and Teunis, and Cori et al. methods. R_t was also estimated using a transmission dynamics model (R_t^Model-based). Uncertainty and sensitivity analyses were conducted. Correlations between different R_t estimates were assessed by calculating correlation coefficients, and agreements between these estimates were assessed through Bland-Altman plots.

Results

R_t^Empirical captured the evolution of the pandemic through three waves, public health response landmarks, effects of major social events, transient fluctuations coinciding with significant clusters of infection, and introduction and expansion of the Alpha (B.1.1.7) variant. The various estimation methods produced consistent and overall comparable R_t^Empirical estimates with generally large correlation coefficients. The Wallinga and Teunis method was the fastest at detecting changes in pandemic dynamics. R_t^Empirical estimates were consistent whether using time series of symptomatic PCR-confirmed cases, all PCR-confirmed cases, acute-care hospital admissions, or ICU-care hospital admissions, to proxy trends in true infection incidence. R_t^Model-based correlated strongly with R_t^Empirical and provided an average R_t^Empirical.

Conclusions

R_t estimations were robust and generated consistent results regardless of the data source or the method of estimation. Findings affirmed an influential role for R_t estimations in guiding national responses to the COVID-19 pandemic, even in resource-limited settings.

Introduction and expansion of the SARS-CoV-2 B.1.1.7 variant and reinfections in Qatar: A nationally representative cohort study

BACKGROUND

The epidemiology of the SARS-CoV-2 B.1.1.7 (or Alpha) variant is insufficiently understood. This study's objective was to describe the introduction and expansion of this variant in Qatar and to estimate the efficacy of natural infection against reinfection with this variant.

METHODS AND FINDINGS

Reinfections with the B.1.1.7 variant and variants of unknown status were investigated in a national cohort of 158,608 individuals with prior PCR-confirmed infections and a national cohort of 42,848 antibody-positive individuals. Infections with B.1.1.7 and variants of unknown status were also investigated in a national comparator cohort of 132,701 antibody-negative individuals. B.1.1.7 was first identified in Qatar on 25 December 2020. Sudden, large B.1.1.7 epidemic expansion was observed starting on 18 January 2021, triggering the onset of epidemic's second wave, 7 months after the first wave. B.1.1.7 was about 60% more infectious than the original (wild-type) circulating variants. Among persons with a prior PCR-confirmed infection, the efficacy of natural infection against reinfection was estimated to be 97.5% (95% CI: 95.7% to 98.6%) for B.1.1.7 and 92.2% (95% CI: 90.6% to 93.5%) for variants of unknown status. Among antibody-positive persons, the efficacy of natural infection against reinfection was estimated to be 97.0% (95% CI: 92.5% to 98.7%) for B.1.1.7 and 94.2% (95% CI: 91.8% to 96.0%) for variants of unknown status. A main limitation of this study is assessment of reinfections based on documented PCR-confirmed reinfections, but other reinfections could have occurred and gone undocumented.

CONCLUSIONS

In this study, we observed that introduction of B.1.1.7 into a naïve population can create a major epidemic wave, but natural immunity in those previously infected was strongly associated with limited incidence of reinfection by B.1.1.7 or other variants.

Estimates of global SARS-CoV-2 infection exposure, infection morbidity, and infection mortality rates in 2020

We aimed to estimate, albeit crudely and provisionally, national, regional, and global proportions of respective populations that have been infected with SARS-CoV-2 in the first year after the introduction of this virus into human circulation, and to assess infection morbidity and mortality rates, factoring both documented and undocumented infections. The estimates were generated by applying mathematical models to 159 countries and territories. The percentage of the world's population that has been infected as of 31 December 2020 was estimated at 12.56% (95% CI: 11.17-14.05%). It was lowest in the Western Pacific Region at 0.66% (95% CI: 0.59-0.75%) and highest in the Americas at 41.92% (95% CI: 37.95-46.09%). The global infection fatality rate was 10.73 (95% CI: 10.21-11.29) per 10,000 infections. Globally per 1000 infections, the infection acute-care bed hospitalization rate was 19.22 (95% CI: 18.73-19.51), the infection ICU bed hospitalization rate was 4.14 (95% CI: 4.10-4.18). If left unchecked with no vaccination and no other public health interventions, and assuming circulation of only wild-type variants and no variants of concern, the pandemic would eventually cause 8.18 million deaths (95% CI: 7.30-9.18), 163.67 million acute-care hospitalizations (95% CI: 148.12-179.51), and 33.01 million ICU hospitalizations (95% CI: 30.52-35.70), by the time the herd immunity threshold is reached at 60-70% infection exposure. The global population remained far below the herd immunity threshold by end of 2020. Global epidemiology reveals immense regional variation in infection exposure and morbidity and mortality rates.

Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept

Geospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and population-level risk assessment. For proof of concept, we adapted a spatially explicit COVID-19 model to investigate a hypothetical geospatial targeting of COVID-19 vaccine rollout in Ohio, United States, at the early phase of COVID-19 pandemic. The population-level deterministic compartmental model, incorporating spatial-geographic components at the county level, was formulated using a set of differential equations stratifying the population according to vaccination status and disease epidemiological characteristics. Three different hypothetical scenarios focusing on geographical subpopulation targeting (areas with high versus low infection intensity) were investigated. Our results suggest that a vaccine program that distributes vaccines equally across the entire state effectively averts infections and hospitalizations (2954 and 165 cases, respectively). However, in a context with equitable vaccine allocation, the number of COVID-19 cases in high infection intensity areas will remain high; the cumulative number of cases remained >30,000 cases. A vaccine program that initially targets high infection intensity areas has the most significant impact in reducing new COVID-19 cases and infection-related hospitalizations (3756 and 213 infections, respectively). Our approach demonstrates the importance of factoring geospatial attributes to the design and implementation of vaccination programs in a context with limited resources during the early stage of the vaccine rollout.

SARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates in Qatar

The SARS-CoV-2 pandemic resulted in considerable morbidity and mortality as well as severe economic and societal disruptions. Despite scientific progress, true infection severity, factoring both diagnosed and undiagnosed infections, remains poorly understood. This study aimed to estimate SARS-CoV-2 age-stratified and overall morbidity and mortality rates based on analysis of extensive epidemiological data for the pervasive epidemic in Qatar, a country where < 9% of the population are ≥ 50 years. We show that SARS-CoV-2 severity and fatality demonstrate a striking age dependence with low values for those aged < 50 years, but rapidly growing rates for those ≥ 50 years. Age dependence was particularly pronounced for infection criticality rate and infection fatality rate. With Qatar's young population, overall SARS-CoV-2 severity and fatality were not high with < 4 infections in every 1000 being severe or critical and < 2 in every 10,000 being fatal. Only 13 infections in every 1000 received any hospitalization in acute-care-unit beds and < 2 in every 1000 were hospitalized in intensive-care-unit beds. However, we show that these rates would have been much higher if Qatar's population had the demographic structure of Europe or the United States. Epidemic expansion in nations with young populations may lead to considerably lower disease burden than currently believed.

Epidemiological impact of prioritising SARS-CoV-2 vaccination by antibody status: mathematical modelling analyses

BACKGROUND

Vaccines against SARS-CoV-2 have been developed, but their availability falls far short of global needs. This study aimed to investigate the impact of prioritising available doses on the basis of recipient antibody status, that is by exposure status, using Qatar as an example.

METHODS

Vaccination impact (defined as the reduction in infection incidence and the number of vaccinations needed to avert one infection or one adverse disease outcome) was assessed under different scale-up scenarios using a deterministic meta-population mathematical model describing SARS-CoV-2 transmission and disease progression in the presence of vaccination.

RESULTS

For a vaccine that protects against infection with an efficacy of 95%, half as many vaccinations were needed to avert one infection, disease outcome or death by prioritising antibody-negative individuals for vaccination. Prioritisation by antibody status reduced incidence at a faster rate and led to faster elimination of infection and return to normalcy. Further prioritisation by age group amplified the gains of prioritisation by antibody status. Gains from prioritisation by antibody status were largest in settings where the proportion of the population already infected at the commencement of vaccination was 30%-60%. For a vaccine that only protects against disease and not infection, vaccine impact was reduced by half, whether this impact was measured in terms of averted infections or disease outcomes, but the relative gains from using antibody status to prioritise vaccination recipients were similar.

CONCLUSIONS

Major health and economic gains can be achieved more quickly by prioritizing those who are antibody-negative while doses of the vaccine remain in short supply.

Characterizing the Qatar advanced-phase SARS-CoV-2 epidemic

The overarching objective of this study was to provide the descriptive epidemiology of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in Qatar by addressing specific research questions through a series of national epidemiologic studies. Sources of data were the centralized and standardized national databases for SARS-CoV-2 infection. By July 10, 2020, 397,577 individuals had been tested for SARS-CoV-2 using polymerase-chain-reaction (PCR), of whom 110,986 were positive, a positivity cumulative rate of 27.9% (95% CI 27.8-28.1%). As of July 5, case severity rate, based on World Health Organization (WHO) severity classification, was 3.4% and case fatality rate was 1.4 per 1,000 persons. Age was by far the strongest predictor of severe, critical, or fatal infection. PCR positivity of nasopharyngeal/oropharyngeal swabs in a national community survey (May 6-7) including 1,307 participants was 14.9% (95% CI 11.5-19.0%); 58.5% of those testing positive were asymptomatic. Across 448 ad-hoc testing campaigns in workplaces and residential areas including 26,715 individuals, pooled mean PCR positivity was 15.6% (95% CI 13.7-17.7%). SARS-CoV-2 antibody prevalence was 24.0% (95% CI 23.3-24.6%) in 32,970 residual clinical blood specimens. Antibody prevalence was only 47.3% (95% CI 46.2-48.5%) in those who had at least one PCR positive result, but 91.3% (95% CI 89.5-92.9%) among those who were PCR positive > 3 weeks before serology testing. Qatar has experienced a large SARS-CoV-2 epidemic that is rapidly declining, apparently due to growing immunity levels in the population.

Characterizing the Qatar advanced-phase SARS-CoV-2 epidemic

The overarching objective of this study was to provide the descriptive epidemiology of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in Qatar by addressing specific research questions through a series of national epidemiologic studies. Sources of data were the centralized and standardized national databases for SARS-CoV-2 infection. By July 10, 2020, 397,577 individuals had been tested for SARS-CoV-2 using polymerase-chain-reaction (PCR), of whom 110,986 were positive, a positivity cumulative rate of 27.9% (95% CI 27.8-28.1%). As of July 5, case severity rate, based on World Health Organization (WHO) severity classification, was 3.4% and case fatality rate was 1.4 per 1,000 persons. Age was by far the strongest predictor of severe, critical, or fatal infection. PCR positivity of nasopharyngeal/oropharyngeal swabs in a national community survey (May 6-7) including 1,307 participants was 14.9% (95% CI 11.5-19.0%); 58.5% of those testing positive were asymptomatic. Across 448 ad-hoc testing campaigns in workplaces and residential areas including 26,715 individuals, pooled mean PCR positivity was 15.6% (95% CI 13.7-17.7%). SARS-CoV-2 antibody prevalence was 24.0% (95% CI 23.3-24.6%) in 32,970 residual clinical blood specimens. Antibody prevalence was only 47.3% (95% CI 46.2-48.5%) in those who had at least one PCR positive result, but 91.3% (95% CI 89.5-92.9%) among those who were PCR positive > 3 weeks before serology testing. Qatar has experienced a large SARS-CoV-2 epidemic that is rapidly declining, apparently due to growing immunity levels in the population.

Epidemiological Differences in the Impact of COVID-19 Vaccination in the United States and China

This study forecasts Coronavirus Disease 2019 (COVID-19) vaccination impact in two countries at different epidemic phases, the United States (US) and China. We assessed the impact of both a vaccine that prevents infection (VES of 95%) and a vaccine that prevents only disease (VEP of 95%) through mathematical modeling. For VES of 95% and gradual easing of restrictions, vaccination in the US reduced the peak incidence of infection, disease, and death by >55% and cumulative incidence by >32% and in China by >77% and >65%, respectively. Nearly three vaccinations were needed to avert one infection in the US, but only one was needed in China. For VEP of 95%, vaccination benefits were half those for VES of 95%. In both countries, impact of vaccination was substantially enhanced with rapid scale-up, vaccine coverage >50%, and slower or no easing of restrictions, particularly in the US. COVID-19 vaccination can flatten, delay, and/or prevent future epidemic waves. However, vaccine impact is destined to be heterogeneous across countries because of an underlying "epidemiologic inequity" that reduces benefits for countries already at high incidence, such as the US. Despite 95% efficacy, actual vaccine impact could be meager in such countries if vaccine scale-up is slow, acceptance is poor, or restrictions are eased prematurely.

Vulnerability of Syrian refugees in Lebanon to COVID-19: quantitative insights

Lebanon, a middle-income country with ongoing political turmoil, unstable economic situation, and a fragmented and under-resourced health system, hosts about one million Syrian refugees since 2011. While the country is currently experiencing substantial COVID-19 epidemic spread, no outbreaks have been reported yet among Syrian refugees. However, testing of this population remains limited and exposure levels are high given dire living conditions and close interaction with the host community. Here, we use quantitative insights of transmission dynamics to outline risk and contextual factors that may modulate vulnerability of Syrian refugees in Lebanon to potentially large COVID-19 epidemics.

Syrian refugees live in close contact with the host community, and their living conditions are favorable for epidemic spread. We found that the high levels of crowding within Syrian refugee households and among those in informal tented settlements, the inadequate water supply and sanitation, limited use of masks, inadequate access to health care, and inadequate community awareness levels are vulnerability factors that directly impact important parameters of transmission dynamics, leading to larger epidemic scale. Poverty, stigma, and fear of legal consequences are contextual factors that further exacerbate this vulnerability. The relatively high prevalence of non-communicable diseases in this population could also affect the severity of the disease among those infected. Mathematical modeling simulations we conducted illustrated that even modest increases in transmission among Syrian refugees could result in a large increase in the incidence and cumulative total number of infections in the absence of interventions.

In conclusion, while the young age structure of the Syrian refugee population might play a protective role against the scale and disease-burden severity of a potential COVID-19 epidemic, the epidemic potential due to several vulnerability factors warrants an immediate response in this population group. Local and international actors are required to mobilize and coordinate efforts to prevent the transmission of COVID-19, and to mitigate its impact amongst the vulnerable refugee populations globally.

Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19

BACKGROUND

Mathematical modeling constitutes an important tool for planning robust responses to epidemics. This study was conducted to guide the Qatari national response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. The study investigated the epidemic's time-course, forecasted health care needs, predicted the impact of social and physical distancing restrictions, and rationalized and justified easing of restrictions.

METHODS

An age-structured deterministic model was constructed to describe SARS-CoV-2 transmission dynamics and disease progression throughout the population.

RESULTS

The enforced social and physical distancing interventions flattened the epidemic curve, reducing the peaks for incidence, prevalence, acute-care hospitalization, and intensive care unit (ICU) hospitalizations by 87%, 86%, 76%, and 78%, respectively. The daily number of new infections was predicted to peak at 12 750 on May 23, and active-infection prevalence was predicted to peak at 3.2% on May 25. Daily acute-care and ICU-care hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction number R0 had varied between 1.07-2.78, and 50.8% of the population were estimated to have been infected (1.43 million infections). The proportion of actual infections diagnosed was estimated at 11.6%. Applying the concept of Rt tuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, when Rt declined to 0.7, to buffer the increased interpersonal contact with easing of restrictions and to minimize the risk of a second wave. No second wave has materialized as of October 15, 2020, five months after the epidemic peak.

CONCLUSIONS

Use of modeling and forecasting to guide the national response proved to be a successful strategy, reducing the toll of the epidemic to a manageable level for the health care system.